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Abstract

The measure of performance most often associated with fuel cells is a polarization curve, which describes the output voltage over a range of current densities for a specific set of operating conditions.
Presently implemented and widely accepted method for characterization of the fuel cell stack performance is polarization curve (stack voltage vs. stack current).
The proposed solution utilizes self-organizing neural network that is fed data from the dynamic drive, and therefore does not require facilitated test under strict conditions. The self-organizing neural network "unfolds" itself in the Voltage-Current plane and based on the dynamic data provided, it takes shape of the polarization curve. This would enable easier and less time consuming polarization curve acquisition, leading to the easier fuel cell performance degradation monitoring.

Country

United States

Language

English (United States)

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This is the abbreviated version, containing approximately
71% of the total text.

The
measure of performance most often associated with fuel cells is a polarization
curve, which describes the output voltage over a range of current densities for
a specific set of operating conditions.

Presently
implemented and widely accepted method for characterization of the fuel cell
stack performance is polarization curve (stack voltage vs. stack current). This
polarization curve depends on several parameters (temperatures, pressures, fuel
and air flows). The method that is used for polarization curve estimation
consists of running a warm fuel cell stack according to the pre determined load
profile, set stack current, wait for
certain amount of time (usually minutes) for voltage to stabilize, record the
voltage and current, and move to the next load point. This procedure is
somewhat difficult to implement on the vehicle. Even in the controlled
environment, achieving a fixed load points can be difficult due to the interaction
of the other systems (fans may start or stop, cooling pumps may go on or off,
etc).

The proposed solution utilizes
self-organizing neural network that is fed data from the dynamic drive, and
therefore does not require facilitated test under strict conditions. The
self-organizing neura...